Clustering, robust statistics, geometric processing and graph processing are commonly used to analyze spatial data points. For example, k-means clustering can be applied to color dusters to derive color palettes, to extrapolate color using the median matrix, to apply a computer vision algorithm for fitting of geometric primitives, and to perform a graph formulation of color categories. The color clustering assumes uniform, symmetric distributions and that competing methods of computing k are sub-optimal. The color extrapolation may use a variant of a Theil-Sen estimator. The geometric primitives technique may be ineffective at processing complex contours. Finally, the results from color categorization networks show the power and expressiveness of graph formulations of data with a spatial interpretation.